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Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

机译：基于有限元和神经网络模型的车载耦合激发，长跨度桥梁振动特性的数值计算

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Ling-bo Wang; Pei-wen Jiang;
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1. Virtual reality research on vibration characteristics of long-span bridges with considering vehicle and wind loads based on neural networks and finite element method [J] . Feng-hui Dong Neural computing & applications . 2018,第5期

机译：基于神经网络和有限元方法考虑车辆和风力负荷的长跨度桥梁振动特性的虚拟现实研究
2. Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements [J] . Hao Wang, Ai-qun Li, Jian Li Engineering Structures . 2010,第9期

机译：基于环境振动和静态测量的大跨度悬索桥渐进有限元模型标定
3. Neural networks-based damage detection for bridges considering errors in baseline finite element models [J] . Lee JJ, Lee JW, Yi JH, Journal of Sound and Vibration . 2005,第3a5期

机译：考虑基线有限元模型误差的桥梁神经网络损伤检测
4. CONDITION-ASSESSMENT-BASED FINITE ELEMENT MODELING OF LONG-SPAN BRIDGE BY MIXED DIMENSIONAL COUPLING METHOD [C] . T. H. T. Chan, Y. Yu, K.Y. Wong, the 3rd World Congress on Engineering Asset Management andIntelligent Maintenance Systems（第三届世界工程资产管理及智能维修学术大会）论文集 . 2008

机译：基于条件评估的大跨度桥梁有限元建模的混合维数耦合方法
5. Development of an integrated approach combining artificial neural network material based modeling with finite element analysis of forming processes. [D] . Kessler, Brian Scott. 2005

机译：集成方法的开发，该方法将基于人工神经网络材料的建模与成形过程的有限元分析相结合。
6. Experimental Study on Vibration Characteristics of Unit-Plate Ballastless Track Systems Laid on Long-Span Bridges Using Full-Scale Test Rigs [O] . Weiqi Zheng, Xingwang Sheng, Zhihui Zhu, 2020

机译：大跨度桥梁在大跨度桥梁上单板无Ball轨道系统振动特性试验研究
7. Condition-assessment-based finite element modeling of long-span bridge by mixed dimensional coupling method [O] . Chan Tommy H.T., Yu Yang, Wong K.Y., 2008

机译：基于条件评估的大跨度桥梁有限元建模的混合维耦合方法

Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

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